JSON Schema Validator
Welcome to our JSON Schema Validator. Ensure your JSON data conforms to predefined schemas for data integrity and consistency. This tool is essential for developers, data analysts, and anyone working with JSON data.
How the JSON Schema Validator checks data
The JSON Schema Validator checks a JSON document against a JSON Schema and reports either Valid or Invalid with the location of each problem, written as a JSON Pointer such as /age. A schema without a $schema keyword is read as draft-07.
Validation uses the Opis JSON Schema library. Both inputs first go through the same repair step as JSON Repair, so a trailing comma does not stop the check. A $ref to a schema on another server is not downloaded and is reported as an unresolved reference.
Worked examples
Data that matches
Both required properties are present and age is a non-negative integer.
- Schema
{ "type": "object", "required": ["name", "age"], "properties": { "name": {"type": "string"}, "age": {"type": "integer", "minimum": 0} } }- JSON
{"name": "Ada", "age": 36}- Result
✅ Valid
A value below the minimum
Same schema as above. The error points at /age.
- JSON
{"name": "Ada", "age": -1}- Result
❌ Invalid
{ "/age": [ "Number must be greater than or equal to 0" ] }
A missing required property
Same schema as above. A missing property is reported on the object that should contain it, here the root, written as /.
- JSON
{"age": 36}- Result
❌ Invalid
{ "/": [ "The required properties (name) are missing" ] }
Common draft-07 keywords
These keywords cover most everyday schemas. All of them are defined in the JSON Schema Validation specification for draft-07.
| Keyword | What it checks | Example |
|---|---|---|
type | The JSON type: string, number, integer, boolean, object, array or null. | {"type": "string"} |
required | Properties that must be present in an object. | {"required": ["id"]} |
properties | A schema for each named property. | {"properties": {"id": {"type": "integer"}}} |
additionalProperties | Whether properties not listed in properties are allowed. | {"additionalProperties": false} |
enum | The value must equal one of the listed values. | {"enum": ["draft", "published"]} |
minimum, maximum | Inclusive lower and upper bounds for numbers. | {"minimum": 0, "maximum": 100} |
minLength, maxLength | Bounds for the number of characters in a string. | {"maxLength": 60} |
pattern | A regular expression the string must match. | {"pattern": "^[a-z-]+$"} |
items | A schema every array element must match. | {"items": {"type": "string"}} |
How to Use the JSON Schema Validator
- Enter your JSON data in the input field.
- Provide the corresponding JSON schema.
- Click the 'Validate JSON Schema' button to check the data against the schema.
Benefits of Using Our JSON Schema Validator
- Ensure Data Integrity: Validates JSON data against schemas to maintain data quality.
- Consistent Data: Ensures your data conforms to expected structures across applications.
- User-Friendly: Simple interface for easy validation.
Frequently asked questions
How do I validate JSON against a schema?
To validate JSON against a schema, enter your JSON data in the input field and provide the corresponding JSON schema. Click the Validate JSON button, and the tool will check the data against the schema, displaying any validation errors.
What is a JSON schema?
A JSON schema is a format for defining the structure, content, and constraints of JSON data. It specifies the expected data types, required fields, and other validation rules to ensure the JSON data is correctly formatted and consistent.
Why is JSON schema validation important?
JSON schema validation is crucial for ensuring data integrity and consistency across applications. It helps catch errors early in the development process, prevents data corruption, and ensures that APIs and other systems handle data correctly.
Can I validate complex JSON structures?
Yes, the JSON Schema Validator can handle complex JSON structures, including nested objects, arrays, and various data types, ensuring thorough validation against your schema.
What happens if my JSON data doesn't match the schema?
If your JSON data doesn't match the schema, the validator will display detailed error messages indicating the issues. This helps you identify and fix the errors to ensure your data conforms to the expected structure.
Which JSON Schema version is supported?
The validator typically supports recent JSON Schema drafts (Draft 4, Draft 6, Draft 7, 2019-09, 2020-12). Each version introduces enhanced features and improved validation capabilities, ensuring compatibility with modern schema definitions.
How do I create a JSON schema?
Define your expected JSON structure using JSON Schema syntax: specify required properties, data types (string, number, boolean, array, object), constraints (min/max, patterns, enums), and nesting. Utilize schema generators or write manually, then test with real data.
Does schema validation replace unit testing?
Schema validation complements but doesn't replace unit tests. It validates data structure and types but not business logic, calculations, or workflows. Use schema validation for API contracts and data integrity; unit tests for application logic and behavior.
How does validation benefit API development?
Schema validation ensures API requests and responses match expected formats, catches malformed data before processing, clearly documents API contracts, enables automatic documentation generation, supports API versioning, and improves integration reliability through boundary validation.
What are typical validation errors?
Common errors include type mismatches (string vs number), missing required properties, unexpected additional properties, values outside allowed enums, pattern mismatches, array length violations, number range violations, and nested object validation failures with specific error details.
Can schemas validate form inputs?
Yes, JSON schemas excel at form validation. Define field types, required fields, validation rules, min/max values, and patterns in schemas. Many form libraries support JSON Schema for declarative validation, reducing manual validation code.
How do I validate arrays using schemas?
Define array schemas with type: 'array', specify item schemas with 'items', set constraints like minItems/maxItems, enforce unique values with uniqueItems, and validate nested array structures. The validator ensures all array elements meet schema requirements.
What is schema-first development?
Schema-first development defines data schemas before implementing code. Create JSON schemas documenting expected data structures, use them to validate all inputs/outputs, generate code from schemas, and ensure all components use consistent data contracts, reducing integration issues.
Can schemas validate conditional requirements?
Yes, JSON Schema supports conditional validation through keywords like if/then/else, dependencies, oneOf, anyOf, and allOf. These enable complex validation logic where certain properties are required only when specific conditions are met.